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Conclusion
Published in Vladimir Raizer, Isaac Elishakoff, Philosophies of Structural Safety and Reliability, 2022
Vladimir Raizer, Isaac Elishakoff
Now, let us discuss opposition to fuzzy sets-based design. Rudolf Kalman (1930–2016) (see his paper of 1972) stated (Zadeh, 2004): Let me say quite categorically that there is no such thing as a fuzzy concept … We do talk about fuzzy things, but they are not scientific concepts. Some people in the past have discovered certain interesting things, formulated their findings in a non-fuzzy way, and therefore we have progressed in science.
Foundation of Fuzzy Systems
Published in Hongxing Li, C.L. Philip Chen, Han-Pang Huang, Fuzzy Neural Intelligent Systems, 2018
Hongxing Li, C.L. Philip Chen, Han-Pang Huang
A concept without a crisp extension is a fuzzy concept. We now ask if a fuzzy concept can be rigidly described by Cantor’s notion of sets or the bivalent (true/false or two-valued) logic. We will show that the answer is negative via the “baldhead paradox”. Since one single hair change does not distinguish a man from his baldheaded status, we have the following postulate:
Captive offshoring drivers in the manufacturing industry: criteria and sub-criteria that influence the location choice
Published in International Journal of Production Research, 2021
Fabio De Felice, Antonella Petrillo, Laura Petrillo
The present research proposes an integrated model that evaluates, through a mathematical multi-criteria decision-making approach, the best relocation for a manufacturing company that is a typical competitive strategic phenomenon in both the domestic and international marketplaces. The model provides meaningful theoretical and managerial contributions. Furthermore, our model allows us to test the potential choices that a decision to captive offshore encompasses considering specific aspect such as costs, labour, infrastructure, etc. More specifically, the research investigates the key criteria and sub-criteria to pursue captive offshoring strategy by combining Delphi and FAHP methods. The power of the Delphi approach is that it provides more understanding of complex problems than other survey techniques. FAHP approach proved to be a convenient method in tackling practical multi-criteria decision-making problems. FAHP is employed to calculate the weights of these criteria. A total of 40 factors are analysed through experts’ opinions investigation. Experts from academia and from industry were interviewed. The uncertainty of human decision making is considered through the fuzzy concept in fuzzy environment. The main contributions of this research are twofold. Firstly, it is to elaborate a multicriteria model for evaluating captive offshoring options during the ‘planning’ phase of a process. Secondly, to identify the best practices for captive offshoring strategy. The developed framework not only brings together academics and industrialists, it also allows companies to prepare and evaluate decisions. In other words, the proposed model represents a valid decision support tool for managers to maintain a high level of company competitiveness in a global market. In this research, the model has been applied in an Italian manufacturing company. Results have been technically validated, through the implementation of a simulation model. The proposed model is a general model that can be extended in different industrial sectors to solve a large-scale of real-world scenarios. Further studies using other methodologies such as hybrid neuro-fuzzy models and different and more detailed case studies are under study and in progress to take the subject forward and to handle uncertainty level of the decision environment.
Efficient key frame extraction and hybrid wavelet convolutional manta ray foraging for sports video classification
Published in The Imaging Science Journal, 2023
Here, the fuzzy concept is presented according to the if-then rules expressing heuristics and knowledge. In the EO procedure, the constraints of and are stable to 2 and 1, individually. Therefore, modifying the constraints of and , the fuzzy system (FS) provides superior outcomes for the execution period. Also, the diversity and iteration parameters are considered for algorithm implementation. These factors are employed for the implementation period to control the constraints of the algorithms. The parameters of and are the major significance of determining the particles’ location in the EO algorithm. Therefore, fuzzy constraints are introduced that are dynamically changed. In every iteration, good solutions are accomplished by dynamically varying the values of constraints. These modified fuzzy parameters are given by: Here, in the FS, the entire number of rules is defined as and . For a rule, the outcome is represented as and . The membership function (MF) related to the rule is defined as and . Initially, in FS, the input variable is iteration, and the output variable is , and constraints are deliberated. Secondly, the diversity and the iteration are used as the inputs, and constraints are used as the outputs. The input variables are separated into three Triangular MF, such as low, high, and medium, in the range of 0–1. The triangular MF can be given as: The iteration is stabilized for the entire number of iterations to get the fraction of the present iteration. The diversity represents the mean of the Euclidean distance among all the particles, and the finest particle is determined. After that, the value of is updated by: In EO, the rate of generation is the major element that leads to the stage of exploitation as given by: